Retail shrinkage is becoming a huge problem for retail merchants (e.g., stores). Retail shrinkage can be attributed to factors such as employee theft, shoplifting, fraudulent or unintentional administrative error, vendor fraud, damage in transit or in store, fraudulent or unintentional transactions at the point of sale (POS) (e.g. by the cashier) etc. According to retailers, most incidents of employee theft occur at point of sale (POS) where store staff use fraudulent means to bypass the barcode registration of retail goods. For instance, POS personnel at a POS site in a store may permit an individual (e.g., a friend or family member of the POS personnel) to move through the POS site with retail goods from the store without paying for the goods or after paying a reduced amount for the goods. This causes retail loss for organizations, and further hampers growth of the organization.
Shrinkage loss prevention measures involves some robust processes to detect fraud or theft, often backed by enforcement and recovery. These include video surveillance at POS, installation of theft deterrent devices, EAS alarms at entrances etc. These measures are generally implemented to identify fraudulent transactions by capturing and recording behavior deviations with video systems for subsequent analysis. But, any sort of human review and analysis of video data is time consuming and inefficient when the scale of data becomes large which is inevitable in today's scenario.
Accordingly, detecting and preventing this retail shrinkage may be desirable for retail merchants. Existing techniques to address the problem of retail shrinkage caused by employee theft at a POS uses techniques such as voting mechanism to classify a pattern into either a fraudulent transaction or a genuine one. The voting mechanism involves frequency-based, SVM-based (Support Vector Machines) or a combination of SUV and frequency based techniques. These techniques along with pattern classification techniques are used to classify any new pattern into fraud and genuine classes. However, the voting mechanism based on frequency has been observed to be inaccurate as it causes false alarms, which leads to unnecessary chaos. Further, it has been observed that with existing methods involving use of pattern recognition techniques, incidence of false alarms and inaccurate detection or lack of detection of fraudulent transactions is widespread.
In light of the above drawbacks, there is a need for a system and a method which accurately detects fraudulent transactions at a transaction site. There is also a need for a system and a method which maximizes true detection of any fraud transaction and minimizes any sort of false alarms. Further, there is a need for a system and a method which is capable of analyzing pattern of events associated with one or more transactions in real time using surveillance data and transaction log data and detect fraudulent transactions. Yet further, there is a need for a system and a method which can be easily implemented at a Point of Sale (POS) with concentration on retail shrinkage primarily due to theft of inventory by employees.